A rational route to probing membrane proteins
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Genome Biology. 2007 May 31;8(5):214
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http://dx.doi.org/10.1186/gb-2007-8-5-214
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BioMed Central Ltd
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Wed May 25 18:19:26 EDT 2016
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http://hdl.handle.net/1721.1/58706
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A rational route to probing membrane proteins
Amy E Keating
Address: MIT Department of Biology, Massachusetts Avenue, Cambridge, MA 02139, USA. Email: keating@mit.edu
Published: 31 May 2007
Genome Biology 2007, 8:214 (doi:10.1186/gb-2007-8-5-214)
The electronic version of this article is the complete one and can be
found online at http://genomebiology.com/2007/8/5/214
© 2007 BioMed Central Ltd
Abstract
A recent report describes the design of short peptides that bind specifically to transmembrane
regions of integrins, providing an exciting tool for probing the biology of membrane proteins.
Membrane proteins constitute around 20-30% of most
proteomes. They carry out numerous critical functions and
are significantly over-represented as drug targets compared
with soluble proteins. However, membrane proteins present
a host of practical challenges that have limited our understanding of their structure-function relationships. Methods
that are standard for investigating the interactions among
soluble proteins, such as phage display, yeast two-hybrid
analysis, or any experiment that requires specific antibodies,
are difficult or impossible to apply to transmembrane
regions of membrane proteins. This makes it hard to probe
the effects of specifically inhibiting or activating proteins
that reside within the membrane. New reagents and
approaches for deciphering membrane protein function
could significantly advance our understanding.
Given the difficulties of experimentally selecting probes
specific for membrane proteins, the rational design of such
molecules is appealing. In particular, computational protein
design holds promise for providing micro-scale tools
appropriate for manipulating the molecular world. Successes
in designing protein sequences that adopt desired folds,
specifically recognize small molecules or catalyze reactions
have raised hopes that rational design may provide a route
to useful reagents and therapeutics [1-7]. The obstacles that
confront the field are significant, however. In particular, the
challenge of designing proteins or peptides to bind tightly
and specifically to native protein targets is largely unmet,
although this is arguably one of the areas where the impact
of protein design could be greatest. Two big problems
confront protein engineers. One is the vast sequence/structure space in which possible solutions lie (the ‘search
problem’). The other is the physics of molecular recognition,
which is complex and has proved difficult to capture in
computational methods that are fast enough to use for
design (the ‘energy problem’).
There are theoretical reasons why membrane proteins may
present easier targets for design than soluble ones. Both the
search problem and the energy problem are simplified in
membranes. Because of the hydrophobic environment, the
amino-acid alphabet used by the intramembrane regions of
proteins is restricted. The space of possible topologies is also
limited, and the energy terms that are most important for
folding and recognition in membranes are easier to model
than those that are critical for soluble proteins. DeGrado and
co-workers [8] have recently seized on these advantages to
design the first peptide sequences that bind specifically to
transmembrane helices. They designed three CHAMP
peptides (computed helical anti-membrane proteins) that
bind to the cell adhesion molecules integrin αIIb or integrin
αv in vitro, as well as in mammalian cells. This success
supports the idea that membrane proteins are particularly
good targets for computational design, and suggests a bright
future in which biophysical principles, captured in efficient
design algorithms, will provide new opportunities to probe
the biology of membrane proteins.
Challenges and successes in computational design
A series of remarkable results from the computational
protein-design field over the past several years illustrates the
power of a good match between problem and method.
Although it is not yet possible to apply automated methods
to provide any desired function, computational design is well
suited to identifying combinations of amino acids that
stabilize a specified backbone geometry. Sequences that
adopt an impressive range of both native [1,2] and novel
Genome Biology 2007, 8:214
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Keating
[3,4] folds have been successfully engineered. Introducing
function into these folds is more difficult, although Hellinga
and co-workers [5] have developed dynamic receptors that
recognize small molecules via steric complementarity and
appropriate hydrogen bonding using computational
methods. A small number of proteins with enzymatic activity
have also been designed [6,7].
The very small number of successful design projects that
have identified peptides or proteins that bind to native
targets illustrates the difficulty of this problem for soluble
proteins. Nearly a decade ago, Ghirlanda et al. [9] used
computational methods to design a hairpin of helices to bind
a soluble helix comprising the calmodulin-binding domain
of calcineurin, forming a three-helix coiled coil. More
recently, Reina et al. [10] redesigned a PDZ domain to
change its peptide-ligand-binding specificity. And in work
redesigning calmodulin, Mayo and colleagues [11] identified
variants with greater specificity than wild type. In my
laboratory, we have designed novel peptide ligands for the
anti-apoptotic protein Bcl-xL [12].
http://genomebiology.com/2007/8/5/214
association is determined by a delicate balance of enthalpic
and entropic terms, and includes contributions from van der
Waals, electrostatic and solvation energies. All of these are
difficult to model accurately under the approximations that
are typically used in design calculations. Solvation and
electrostatic effects are particularly hard to model in an
aqueous environment [14]. Yin et al. [8] were able to
simplify their membrane design problem by making three
assumptions. The first was that they did not need to
accurately compute interactions between backbone atoms,
for example, interhelical C-H••••O=C hydrogen bonds,
because they restricted their backbone sampling to a few
naturally occurring geometries where these interactions
were already built in. Thus, they did not rely on a
computational energy function to correctly position the
helices with respect to one another. This approach is also
common in the design of soluble proteins. Their second
assumption was membrane-protein-specific, and posited
that a simplified statistical model could be used to capture
solvation effects, as a function of depth in the membrane.
Finally, they assumed that good packing of the side chains
would be sufficient to achieve both affinity and specificity;
given the hydrophobic nature of the side chains and their
environment, electrostatic interactions were not treated
explicitly. This assumption is also more realistic for
membrane proteins than for soluble ones. Yin et al. [8] used
computational analyses guided by these principles and
visual inspection to choose final sequences. Remarkably, this
strategy succeeded in three out of three attempts.
Part of the difficulty of protein design stems from the vast
size of the search spaces. Even short peptides can span an
astronomical sequence space (20N, for a peptide of length N)
and can adopt an essentially infinite number of conformations. In general, only a small fraction of possible sequences
and structures can be considered computationally, and for
soluble proteins this can be very limiting. For membrane
proteins, however, restricting the structure and sequence
space probably poses a less severe approximation. A growing
set of membrane protein structures reveals that α-helical
transmembrane regions pack against one another in a
limited set of geometries; these geometries can be broken
into subsets characterized by the sequence of the protein
[13]. Thus, when Yin et al. [8] sought a template on which to
design peptides to bind to integrin αIIb or integrin αv, both of
which contain a small-X3-small sequence motif, they were
able to consider just 35 appropriate helix-helix pairings
taken from structures in the Protein Data Bank. They tested
five of these in the design of anti-αIIb peptides and 15 for
anti-αv. Membrane proteins also use a limited amino-acid
alphabet compared to soluble proteins, due to the
hydrophobic nature of the lipid membrane in which they
reside. In the CHAMP designs, most of the residues were
selected from a set of just eight amino acids that comprise
75% of membrane-protein residues (Ala, Phe, Gly, Ile, Leu,
Ser, Val and Thr). Thus, the search problem for this design
application was restricted to sampling sequences, and
optimizing side-chain conformations, for combinations of
these residues.
The most notable feature of the designed CHAMP peptides is
that they are specific for their intended targets. This is true
despite the fact that specificity was not explicitly modeled in
the design procedure. The designed peptides did interact
with themselves, as homodimers, but the anti-αIIb peptide
did not bind to integin αv, and the anti-αv peptide did not
bind to integrin αIIb. This was tested in a bacterial dominantnegative assay and also in a single-molecule assay for the
adhesion of platelets to beads coated with fibrinogen (testing
for activation of αIIb) or osteopontin (testing for activation of
αv). The specificity is notable, because the sequences of αIIb
and αv are quite similar (both bind integrin β3), and also
because steric patterning is not a reliable strategy for
engineering specificity into soluble proteins. Specificity is
essential, however, if reagents such as the CHAMP peptides
are to be useful for cellular applications. For example, the
authors point out that their anti-αv peptide had to recognize
αv amid large amounts of αIIb on the cell surface in order to
be effective.
The energy problem in protein design is to determine which
of many possible sequence-structure combinations is lowest
in energy (or has some other desired characteristic). This is
typically very daunting. The physics of protein folding and
A critical question going forward will be the extent to which
specificity against other classes of transmembrane alpha
helices has also been achieved ‘for free’ using this design
procedure. Self-association of the designs suggests that some
Specificity without specific design?
Genome Biology 2007, 8:214
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Genome Biology 2007,
improvements in specificity may be necessary for optimal
efficacy. However, even if it turns out that additional steps
are necessary, such as the explicit consideration of undesired
states in the modeling procedure, this work has
demonstrated the potential of short designer peptides for
providing valuable probes for use in studying membrane
protein function. It has also highlighted the good match
between computational design and membrane targets,
which will no doubt be exploited further in future.
Acknowledgements
I thank Gevorg Grigoryan and James Apgar for thoughtful comments on
the manuscript.
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